There are number of FLUXNET sites, where simultaneous
observations of canopy structure, leaf-area index (LAI) development, net
ecosystem exchange and soil respiration are made. These sites provide an
excellent basis for validating the Moderate Resolution Imaging Spectroradiometer
(MODIS) remote sensing products that are related to ecosystem structure and
carbon and water balance. The MODIS-GPP/NPP algorithm integrates
1°×1°-assimilated meteorological data, the remotely sensed
fraction of photosynthetically active radiation (fPAR) absorbed by the
vegetation, LAI and landcover information to achieve a global estimate of daily
to annual gross and net primary productivity (GPP; NPP) on a 1 km2
grid. Over homogeneous terrain (1 ha to 1 km2), the FLUXNET eddy
covariance stations sample net ecosystem carbon exchange, which can be split
into ecosystem respiration and GPP flux components. Hence, the FLUXNET eddy
covariance stations provide an excellent means to evaluate the MODIS-GPP product
and other remote-sensing-driven carbon balance estimates.

APPLICATION

In GTOS, there is an ongoing effort to compare GPP estimated
using the MODIS sensor with ground-observed data at various European
FLUXNET/CARBOEUROPE eddy covariance tower sites. The sites selected for
comparison range from 38° to 67° N and comprise boreal and temperate
conifer forests (spruce, pine), temperate and Mediterranean deciduous forests
(beech, oak), Mediterranean evergreen broadleaf forests, and a savannah-type
Mediterranean ecosystem.

Figure 1. Net Primary Productivity

The analysis is based on the assumption that a sensible
evaluation of the MODIS-GPP estimate must account for all error sources that
occur during the computation. Thus, in a factorial approach, we analyse (1) the
effect of driving the MODIS-GPP model with 1° by 1° assimilated
meteorological data versus local meteorological data; (2) the error introduced
by remotely sensed estimates of seasonal fPAR/LAI development; and (3) the bias
introduced by the MODIS-GPP radiation-use-efficiency model itself.

OUTCOME

Given the independent nature (not fitted against flux data)
and the simplicity of the MODIS-GPP model, its overall performance in predicting
GPP is remarkable under normal conditions (r2 between 0.7 and 0.95).
The assimilated meteorology does not capture all day-to-day variation, but
matches the local tower data well on an eight-day scale. However, at certain
sites the meteorological bias influences estimates of GPP significantly.
Particularly at high latitudes, the correction of cloud-contaminated fPAR/LAI
values enhances GPP estimates considerably. At sites with understorey or a
herbaceous spring layer, springtime GPP is often overestimated by the MODIS-GPP
model since it cannot account for differences in radiation-use efficiency by
canopy and understorey. Furthermore, there is potential for considerable
improvements of the GPP algorithm by better accounting for soil drought effects,
by reducing the radiation-use efficiency under high-radiation conditions, and by
introducing more geo-biological variability. It has been shown that these parts
of the MODIS-GPP algorithm can be re-parameterized using CARBOEUROPE eddy
covariance data, so the synergistic use of MODIS and CARBOEUROPE data will
improve the ability of a global terrestrial observation system.